Diffusion tensor imaging (DTI) Analysis Based on Tract-based spatial statistics (TBSS) and Classification Using Multi-Metric in Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a brain disorder characterized by atrophy of cerebral cortex and neurofibrillary tangles. Accurate identification of individuals at high risk of developing AD is key to early intervention. Combining neuroimaging markers derived from diffusion tensor images with machine learning techniques, unique anatomical patterns can be identified and further distinguished between AD and healthy control (HC). METHODS: In this study, 37 AD patients (ADs) and 36 healthy controls (HCs) from the Alzheimer's Disease Neuroimaging Initiative were applied to tract-based spatial statistics (TBSS) analysis and multi-metric classification research. RESULTS: The TBSS results showed that the corona radiata, corpus callosum and superior longitudinal fasciculus were the white matter fiber tracts which mainly suffered the severe damage in ADs. Using support vector machine recursive feature elimination (SVM-RFE) method, the classification performance received a decent improvement. In addition, the integration of fractional anisotropy (FA) + mean diffusivity (MD) + radial diffusivity (RD) into multi-metric could effectively separate ADs from HCs. The rank of significance of diffusion metrics was FA > axial diffusivity (DA) > MD > RD in our research. CONCLUSIONS: Our findings suggested that the TBSS and machine learning method could play a guidance role on clinical diagnosis.

Response of Electrical Activity in an Improved Neuron Model under Electromagnetic Radiation and Noise.

Electrical activities are ubiquitous neuronal bioelectric phenomena, which have many different modes to encode the expression of biological information, and constitute the whole process of signal propagation between neurons. Therefore, we focus on the electrical activities of neurons, which is also causing widespread concern among neuroscientists. In this paper, we mainly investigate the electrical activities of the Morris-Lecar (M-L) model with electromagnetic radiation or Gaussian white noise, which can restore the authenticity of neurons in realistic neural network. First, we explore dynamical response of the whole system with electromagnetic induction (EMI) and Gaussian white noise. We find that there are slight differences in the discharge behaviors via comparing the response of original system with that of improved system, and electromagnetic induction can transform bursting or spiking state to quiescent state and vice versa. Furthermore, we research bursting transition mode and the corresponding periodic solution mechanism for the isolated neuron model with electromagnetic induction by using one-parameter and bi-parameters bifurcation analysis. Finally, we analyze the effects of Gaussian white noise on the original system and coupled system, which is conducive to understand the actual discharge properties of realistic neurons.

The Effects of Medium Spiny Neuron Morphologcial Changes on Basal Ganglia Network under External Electric Field: A Computational Modeling Study.

The damage of dopaminergic neurons that innervate the striatum has been considered to be the proximate cause of Parkinson's disease (PD). In the dopamine-denervated state, the loss of dendritic spines and the decrease of dendritic length may prevent medium spiny neuron (MSN) from receiving too much excitatory stimuli from the cortex, thereby reducing the symptom of Parkinson's disease. However, the reduction in dendritic spine density obtained by different experiments is significantly different. We developed a biological-based network computational model to quantify the effect of dendritic spine loss and dendrites tree degeneration on basal ganglia (BG) signal regulation. Through the introduction of error index (EI), which was used to measure the attenuation of the signal, we explored the amount of dendritic spine loss and dendritic trees degradation required to restore the normal regulatory function of the network, and found that there were two ranges of dendritic spine loss that could reduce EI to normal levels in the case of dopamine at a certain level, this was also true for dendritic trees. However, although these effects were the same, the mechanisms of these two cases were significant difference. Using the method of phase diagram analysis, we gained insight into the mechanism of signal degradation. Furthermore, we explored the role of cortex in MSN morphology changes dopamine depletion-induced and found that proper adjustments to cortical activity do stop the loss in dendritic spines induced by dopamine depleted. These results suggested that modifying cortical drive onto MSN might provide a new idea on clinical therapeutic strategies for Parkinson's disease.